Introduction
Nitrogen oxides (NOx = NO + NO2) play a key role in the air quality
of the boundary layer. While NO is produced in combustion processes
(transport, thermal power plants, etc.), NO2 mainly appears through
the reaction of NO with O3 or HO2. Eventually, the photolysis of
NO2 releases an oxygen atom and a NO molecule. To a good
approximation, the balance of NO and NO2 is kept constant
through this cycle of photo-chemical reactions, which substantiates the
widespread use of the NOx family concept .
Considering the relative ease of measuring NO2 with visible-light
spectroscopy, NOx budgets are often inferred based on NO2
measurements and the photochemical equilibrium assumption.
The most common NO2 remote sensing techniques rely on the
differential optical absorption spectroscopy (DOAS), which is based on the
fitting of radiance spectra with the effective absorption cross section of
interfering species e.g.,. If equipped with a 2-D sensor
array, these instruments disperse the light spectrum along one dimension and
record its spatial variation along the other. Building a complete
hyperspectral image requires an incremental depointing of the instantaneous
field of view (FOV) or a translation of the whole instrument. Typical
examples of both applications can be found in or
. While the DOAS technique is well validated in terms of
accuracy and sensitivity, the need for scanning the scene hampers the
detection of dynamic processes. As the scene is sampled slice by slice, the
final image does not show a great temporal consistency: different rows (or
columns, depending on the scanning direction) are temporally disconnected from
each other. The time gap can reach several minutes between both edges of the
scene.
There are situations where high spatiotemporal resolution is needed. In
volcanology, for instance, the so-called SO2 cameras are now
increasingly complementing the measurements performed with classical
dispersive techniques like grating spectrometers .
Their concept is based on taking spectral images of the plume through two
interference filters. One filter selects a narrow band of the incident
spectrum around 310 nm, where SO2 is still strongly absorbing, while the
other one captures the light around 330 nm, where almost no more absorption
takes place. The main advantages are a typical temporal resolution of 1 Hz,
the capability to capture dynamic features such as puffs in the plume and
the possibility to determine the plume speed from the sequence of images. The
disadvantages are the interference by the plume aerosols caused by the coarse
spectral resolution and the need for regular recalibration with reference
cells filled with SO2 to account for changes of illumination
conditions . More recent concepts now use the combined
information of a spectrometer with the camera spectral images
, which yields a greater measurement accuracy.
We present a new instrument, a spectral imager dedicated to measuring the 2-D
NO2 field above finite sources like thermal power plants, industrial
complexes, cities, volcanoes, etc. The measurement principle is close to
the SO2 camera: snapshots at two wavelengths emphasize the presence
of NO2 by taking advantage of absolute differences in the molecule
absorption cross section. Contrary to the SO2 cameras which use
interference filters, the new instrument relies on an acousto-optical tunable
filter (AOTF) to provide the spectral information. The AOTF can offer
sufficient spectral resolution to resolve the structures of the NO2
spectrum. The ability to discriminate between weak and strong absorption
within a few nanometers virtually cuts out any sensitivity to aerosol
scattering and changes of solar angles. Potential applications include urban
and industrial pollution monitoring, emission fluxes estimation,
satellite-product validation and volcanic plume chemistry.
Instrument concept
The AOTF-based NO2 camera springs from the ALTIUS instrument
(atmospheric limb tracker for the investigation of the upcoming stratosphere;
). ALTIUS is a space mission project aimed at the retrieval
of atmospheric species concentration profiles with a global geographical
coverage and a high vertical resolution. Its primary scientific objective is
to measure ozone, but NO2, aerosols, H2O, CH4, polar
stratospheric and noctilucent clouds, and other minor species will be
measured as well. Measurements will be performed in two different geometries:
limb scattering and occultations (Sun, Moon, stars, planets). To address the
problem of tangent height registration of previous limb scatter instruments,
a spectral imager concept based on a tunable filter has been selected. During
the feasibility study, a prototype of the visible (VIS) channel (440–800 nm)
was built from commercially available parts. The detailed description of this
prototype is given in . We will only point out the key
features of the concept.
The instrument images a 6∘ square FOV onto a Princeton Instrument
Pixis 512B peltier-cooled CCD detector (512×512 pixels). The optical
layout (Fig. ) is linear with an intermediate focal plane
located close to the AOTF. To preserve the spectral homogeneity across the
image, the design is made telecentric by placing an iris at the object focal
point of the first lens. This ensures an identical propagation angle of all
light rays through the AOTF.
Optical layout of the NO2 camera seen from top. Light
propagates from left to right through a pupil and a lens doublet, a polarizer
selecting vertically polarized light, the AOTF, a second cross-oriented
polarizer, two lens doublets and the detector.
The most important part of this NO2 camera concept is the AOTF
. AOTFs have been used in many areas requiring spectral
images (agriculture, food industry, fluorescence spectroscopy, etc.) but
received little attention from the atmospheric remote sensing community. The
working principle is based on the interaction of light and sound in a
birefringent crystal (see Fig. ). By the momentum
matching of the optical and acoustic waves, a narrow portion of the light
spectrum is diffracted into a slightly different direction (a few degrees).
If the incident radiation is linearly polarized, the diffracted beam will
leave the crystal with the orthogonal polarization. The spatial and
polarimetric dissociations can be combined to achieve very efficient
extinction of the unwanted spectrum.
Schematics of the acousto-optic interaction in an AOTF (top view).
The gray area depicts the acoustic field created by the piezoelectric
transducer bonded to a lateral face of the TeO2 crystal. The momentum
phase matching of the incident (ki) and diffracted
(kd) photons with the acoustic wave (K) is
represented in the [1‾10] crystallographic frame. The phase
matching takes advantage of the medium birefringence: incident and diffracted
light beams have orthogonal polarizations and different propagation
directions, which facilitates their selection.
The wave vector's matching condition (Fig. 2) creates a monotonic relationship between
the light wavelength and the sound frequency. The acoustic wave is launched
into the crystal by a piezoelectric transducer bonded to one of its facets.
Hence, selecting a particular wavelength λ simply requires us to drive
the transducer to the matching frequency F(λ). The AOTF spectral
transmission function (STF) closely follows a sinc2 shape. The amplitude
of the STF, which determines the filter diffraction efficiency (DE), is
controlled by the acoustic power Pa(λ), which also exhibits
a smooth wavelength dependence. The transducer length defines the length of
the acousto-optic interaction, which directly affects the AOTF bandwidth: a
short transducer will induce a larger passband and vice versa.
The parameters of an AOTF are defined by the crystal elastic and optical
properties and by the propagation directions of light and sound in the frame
of the crystal axes . The AOTF we used was manufactured
out of a TeO2 crystal by the company Gooch & Housego (UK). It offers an
aperture of 10×10 mm2 and a tuning range covering the visible
spectrum. Laboratory characterization revealed a transparency better than
90 % and a DE better than 95 %. In the relevant spectral range for
NO2 measurements, i.e., around 450 nm, the STF showed a bandwidth of
0.6 nm. Typical driving frequencies were around 130 MHz, and less than 100 mW
of acoustic power was needed in any circumstances. The theoretical number of
resolvable spots at 450 nm is about 350 in the plane of acousto-optic
interaction (horizon) and 700 in the vertical direction.
Measurement principle
There are strong similarities between the measurement principles of a
filter-based SO2 camera and an AOTF-based NO2 camera: the FOV
needs to be pointed towards the target region (e.g., a plume) while making
sure that the background can still be seen in some areas of the image. Two
spectral images of the scene are taken: one at a wavelength
λs where there is strong absorption by the target species
and
another at a wavelength λw where there is weak absorption.
In each image, the signal Sij(λ) (in e-) recorded by pixel ij
looking at the plume will be normalized by the background signal
S0(λ) in order to quantify the extinction that took place during the
crossing of the plume. The optical thickness τij associated with the
slant column density (SCD) of the target species observed in the FOV of pixel
ij follows from the comparison of the normalized signals recorded at the
two wavelengths.
The major difference comes from the capability of the AOTF-based NO2
camera to resolve the fine structures of the absorption cross section
σNO2 (Fig. ). This allows choosing
λs and λw very close to each other (a few
nm), minimizing the interference by broadband absorbing and scattering
species like aerosols.
NO2 absorption cross section measured with a Fourier
transform spectrometer (gray line; ) and with this
NO2 camera in the laboratory (red line). At 450 nm, the spectral
resolution of both datasets are 0.04 and 0.6 nm respectively.
Mathematical model
As AOTFs do not treat different polarizations identically, an AOTF-based
NO2 camera exhibits a strong polarization sensitivity. The
polarization state of a stream of light is described by the Stokes vector
s=(I,Q,U,V)T, where I=Ih+Iv and Q=Ih-Iv, with
Ih and Iv being the light intensity along the horizontal and vertical
axes of a scene frame. U and V also refer to the orientation of the
polarization ellipse but they will not be discussed further because they do
not participate if the AOTF and its surrounding polarizers are well aligned.
When light passes through a polarizing part, its Stokes vector can be
changed. A polarizing element is therefore represented by a 4×4
transfer matrix: the Mueller matrix M. A chain of optical
elements is represented by the product of their Mueller matrices. In our
design, the light passes first through a vertical linear polarizer, then the
AOTF, and finally a horizontal linear polarizer. The Stokes vector
representing the light leaving the second polarizer is therefore given by
s′=MPh⋅MAOTF⋅MPv⋅s.
The Mueller matrices of the elements are as follows:
MAOTF=A21-1001-10000000000,MPv=12ηe2+ηt2ηe2-ηt200ηe2-ηt2ηe2+ηt200002ηeηt00002ηeηt,MPh=12ηt2+ηe2ηt2-ηe200ηt2-ηe2ηt2+ηe200002ηeηt00002ηeηt,
where A is the amplitude of the AOTF STF (i.e., its DE, 0≤A≤1),
ηt2 is the attenuation of the light intensity along the polarizer
transmission axis, and ηe2 is the attenuation along the extinction
axis. Assuming that all three elements have their transmission and extinction
axes well aligned, the total Mueller matrix of the camera is simply
M=ηt4⋅MAOTF. As the detector only
measures the total light intensity, the first element of the Stokes vector is
the only meaningful quantity: s′(1)=A⋅ηt4⋅(I-Q)/2=A⋅ηt4⋅Iv. Hence, in the present configuration, the NO2 camera
is only sensitive to vertically polarized component of the light.
We now have a description of the light intensity which will be measured by
the detector, but we still have to account for the transmittance of the
lenses (T) and the quantum efficiency (QE) of the detector. These terms
exhibit a smooth wavelength dependence. For the AOTF STF, one can use
F(λ;λc)=A(λc)⋅G(λ-λc),
where G is essentially a sinc2 function. Moreover, some parameters are
susceptible to vary across the FOV, yielding a pixel-to-pixel variation. This
is particularly true when image planes are located close to optical surfaces
(mainly the AOTF and the detector). Finally, the electronic current (in
e- s-1) found in pixel ij when the AOTF is tuned to λc is
given by
Cij(λc)=∫Aij(λc)⋅ηt4(λ)⋅Iijv(λ)⋅G(λ-λc)⋅T(λ)⋅QEij(λ)dλ,≃Aij(λc)⋅ηt4(λc)⋅T(λc)⋅QEij(λc)∫Iijv(λ)⋅G(λ-λc)dλ,=rij(λc)∫Iijv(λ)⋅G(λ-λc)dλ.
The decision to leave the smoothly varying parameters out of the integral is
supported by the narrow passband of the AOTF (0.6 nm). Their product forms
the instrument response at pixel ij and wavelength λc:
rij(λc). The remaining integral is simply the convolution of the
vertically polarized incident light intensity with the AOTF STF.
Suppose now that pixel ij is looking through an optically thin plume.
NO2 and other species will absorb or scatter photons and decrease the
background light intensity I0v according to the Beer–Lambert law of
extinction:
Iijv(λ)=I0v(λ)⋅exp-τNO2ij(λ)-τ∗ij(λ),
where τNO2ij denotes the plume optical
thickness caused by absorption by NO2 along the light path ending on pixel ij, and τ∗ij is the effective optical
thickness of all other chemical species and particles. Over the passband of
the AOTF, one can consider τ∗(λ) as a constant value
τ∗(λc) and replace τNO2(λ) by its
weighted average:
τ‾NO2(λc)=∫τNO2(λ)⋅G(λ-λc)dλ∫G(λ-λc)dλ.
As the optical thickness is defined by the product of the trace gas SCD k
with its absorption cross section σ, it is clear that
τ‾NO2(λc)=kNO2.σ‾NO2(λc). Under these
assumptions, one can insert Eq. () into Eq. () and write for the pixel photoelectric current:
Cij(λc)=rij(λc)⋅exp-τ‾NO2ij(λc)-τ∗ij(λc)⋅∫I0v(λ)⋅G(λ-λc)dλ.
In the meantime, other pixels have been looking at the unattenuated
background intensity I0. Suppose that one of them is pixel mn. According
to Eq. (), we have
Cmn(λc)=rmn(λc)⋅∫I0v(λ)⋅G(λ-λc)dλ.
Averaging all these background-looking pixels yields the reference current
associated with the background intensity:
C0(λc)=r(λc)⋅∫I0v(λ)⋅G(λ-λc)dλ,
with r representing the average instrument response. Dividing Cij by
C0 yields the transmittance of the plume alone:
Tij(λc)=Cij(λc)rij(λc)C0(λc)r(λc)=exp-τ‾NO2ij(λc)-τ∗ij(λc).
If the spectral interval between λw and λs is small enough
that the approximation τ∗(λw)=τ∗(λs) holds,
then the ratio of the transmittances T(λw)/T(λs) is a
quantity which only depends on the NO2 content of the plume.
Introducing the relative instrument response at pixel ij,
ρij(λ)=rij(λ)/r(λ), we find
Tij(λw)Tij(λs)=Cij(λw)C0(λw)ρij(λw)Cij(λs)C0(λs)ρij(λs)=expτ‾NO2ij(λs)-τ‾NO2ij(λw).
Finally, the NO2 SCD subtended by the area of the plume observed by
pixel ij follows by taking the logarithm of the ratio of transmittances:
kNO2ij=1σ‾NO2(λs)-σ‾NO2(λw)⋅lnTij(λw)Tij(λs).
Clearly, the best sensitivity is reached by maximizing the differential
optical thickness when selecting λw and λs.
Ancillary data
Equations () and () show that the NO2 SCD can be obtained from a
combination of measurements (the detector signal), cross-section data and the
knowledge of the instrument response. In the results presented below, the
cross section is taken from . For the ρij
coefficients, an ad hoc method was set up to build a synthetic flat
field. Taking advantage of a cloudy weather (100 % cloudiness), long-exposure
frames (10 s) were captured at the required wavelengths looking at zenith.
The mean image obtained from tens of such frames constitutes the instrument
response to a synthetic, radiometrically flat scene. This allows us to remove
wavelength-dependent nonuniformities which can be relatively pronounced in,
e.g.,
the AOTF.
Determining the photoelectric current strictly proportional to the signal
(i.e.,
Cij and C0) implies that voltage offset, dark current and stray light
have been subtracted from the raw data. In this respect, AOTFs offer a unique
feature: one can turn them off. This is simply done by bringing the acoustic wave amplitude
to 0. An image acquired in these conditions contains anything but the real
signal (i.e., dark current, offset, stray light). Using Dij and
Dijoff to represent the raw signal of pixel ij (in digital
numbers, DN) when the AOTF is turned on or off respectively, the
photo-electric signal is given by
Sij=Dij-Dijoffγ,
where γ is the sensor gain (in DN/e-). The only precaution is to
take these dark images regularly because the stray light is a function of
the general illumination conditions (e.g., solar angles) and it will vary
with local
time.
Data averaging and multiple image doublets
It is often necessary to repeat the measurements in order to average out
transient features and increase the signal-to-noise ratio. Assuming
that only the plume optical transmission varies, we can write a
time-dependent version of Eq. ():
Cij(λc,t)=rij(λc)⋅exp-τ‾NO2ij(λc,t)-τ∗ij(λc,t)⋅∫I0v(λ)⋅G(λ-λc)dλ.
The time-averaged optical thickness τ(λ,t‾) can be
obtained from the geometric mean of the consecutive images:
∏k=1NCij(λc,tk)N=rij(λc)⋅exp-1N∑k=1Nτ‾NO2ij(λc,tk)+τ∗ij(λc,tk)⋅∫I0v(λ)⋅G(λ-λc)dλ.=rij(λc).exp-τ‾NO2ij(λc,t‾)-τ∗ij(λc,t‾)⋅∫I0v(λ).G(λ-λc)dλ.
Another means of increasing the reliability of the measurements is to use
different doublets, i.e., pairs of λw and λs. If the
transmittance is known for several doublets, their product strengthens the
NO2 SCD retrieval by providing information from multiple spectral
regions. If
ΔσNO2=σ‾NO2(λs)-σ‾NO2(λw), then for two doublets we have for
the SCD
kNO2ij=1ΔσNO2(1)+ΔσNO2(2)⋅lnTij(λw1)⋅Tij(λw2)Tij(λs1)⋅Tij(λs2).
This approach can potentially attenuate a bias in one of the measurements.
Error budget and instrument sensitivity
One can work out Eq. () with the classical
first-order Taylor expansion approximation to determine the uncertainty on
the NO2 SCD. This approach will require estimates of the uncertainty
on the photon counts Cij, on the background signal C0, on the
relative instrument response ρij and on the cross-section data
σNO2. These estimates are not always easily obtained, and
we briefly discuss each of them.
The photo-electric counting rates Cij are obtained from Eq. (): Cij=Sij/t, where t is the sensor exposure time. It
is reasonable to assume that the camera operator selects acquisition
settings to ensure that the signal is well into the shot noise regime:
σCij=Sij/t. With signals exceeding 104 e- in 1 s (the case in the examples below), the relative uncertainty on Cij
will be below 1 %.
The background signal C0 is estimated by averaging the pixels looking at
the background of the scene. While one would presume that the averaging of a
large number of such pixels should yield a very high precision, the accuracy
is limited by the difficulty of identifying pixels effectively looking at the
background. Automated data processing needs a screening of each image to
determine if a pixel is looking at the plume, the background, a cloud or
even a bird. This screening is based on the interpretation of the raw signals
and, for instance, it sometimes fails to recognize pixels which still have in
their FOV the residual NO2 molecules left by a past position of the
plume. From our experience, the relative uncertainty on C0 determined from
a single image is generally larger than 1 % (determined from the sample
standard deviation). Using multiple images, as explained in Sect. , reduces this uncertainty, as C0 is computed for each
image and then averaged. A 1 % total relative error is achievable with a few
images.
The relative instrument response nonuniformity ρij can be obtained
from a homogeneous scene (i.e., a flat field such that
Iij(λ)=I(λ)∀i,j). In this particular case,
ρij(λ)=Cij(λ)/C(λ), where C(λ) is the
average of Cij over a large number of pixels. If the flat field is built
from a number of relatively homogeneous images under the assumption that
their average is truly flat, then the uncertainty on the flatness
participates to the error budget of ρij(λ) and quickly becomes
the driver (signal shot noise is surpassed). This error source is a generic
problem of all imaging systems but remains difficult to quantify. The only
certainty is that it drops with the sample size.
The NO2 absorption cross-section data are taken from
, who report a total relative uncertainty of 3 % at a
resolution of 2 cm-1 (0.04 nm at 450 nm). Taking our coarser resolution
into account (about 0.6 nm), the uncertainty drops to about 0.8 % for the convolved spectrum. However,
the AOTF tuning curve is temperature dependent, with a typical drift of +0.1 nm per Kelvin . The driving electronics is
currently not enslaved to a temperature sensor. The exact measurement
wavelength is computed at the processing stage. Depending on the amount of
wavelength drift, the uncertainty on σNO2(λ) can
reach 5–10 %.
The minimum relative uncertainty on the NO2 SCD will be reached if
the uncertainty on the plume transmittance T is driven by C0. Assuming
σT/T=1 %, and taking into account a 5 % error on the cross-section
term (with a typical value for
σNO2(λs)-σNO2(λw)=2×10-19), one obtains σk=5×1016 molecules cm-2. If
one assumes less favorable conditions like a 1 % uncertainty on ρ,
yielding σT/T=2 % and a 10 % error on the cross section, then the
SCD error reaches 1017 molecules cm-2.
Application to the remote sensing of NO2 at a coal-fired power plant
The data of a spectral imager such as the NO2 camera are more easily
exploited if a number of observational requirements are satisfied. First, the
camera must be placed at a location where both the plume and the background
can be captured within the same image. Second, the target plume must remain
optically thin in order to preserve the assumption of the Beer–Lambert
extinction along a straight light path. Finally, scattered clouds behind the
plume will corrupt the retrieval and should be avoided.
These three requirements were sometimes fulfilled during the second Airborne
ROmanian Measurements of Aerosols and Trace gases (AROMAT-2) campaign in August 2015. The campaign aimed at joining the
efforts of several European research institutes and universities to spatially
and temporally characterize the emissions from two types of sites: a large
city (Bucharest) and point sources (large thermal power plants in the Jiu
Valley, Romania). Both sites should eventually serve as validation targets
for the ESA TROPOMI/S-5P mission.
Observational geometry during the AROMAT-2 campaign at Turceni's
power plant. The NO2 camera was installed on a football pitch looking
at the four 280 m tall stacks. The red lines delimit the camera horizontal
FOV (6∘). The direction of the Sun at 16:00 local time is
approximately indicated, together with two rays illustrating the scattering
behind the scene towards the camera. One of the rays passes through the
plume, while the other one passes by. Map data from OpenStreetMap.
Sample NO2 SCD field obtained from the averaging of images
acquired at λw2=446.7 nm, λs2=448.1 nm,
λw4=465.8 nm and λs4=463.2 nm (12 of
each). The color scale shows the plume NO2 SCD in
molecules cm-2. The x and y axes show the image dimensions in the
scene plane, while the title gives the time span (local time).
The NO2 camera was placed at a distance of 2.5 km from a group of
four
stacks belonging to Turceni's power plant, the largest being in Romania (330 MW
per turbine, 2000 GWh year-1 total electric power generation of which
more than 93 % is generated from coal). Figure
depicts the measurement geometry. Our location was 44.6792∘ N,
23.3788∘ E, the line of sight (LOS) azimuth angle ranged from
113∘ (left edge of the image) to 119∘ (right edge) eastward
from north, and the LOS zenith angle ranged between 75.5∘ (top edge)
and 81.5∘ (bottom edge). We only report on measurements performed on
24 August between 16:15 and 16:30 LT as the
observational conditions were close to ideal and best illustrate the
performance of the instrument. In particular, the smokes were optically thin,
with the blue sky clearly visible in the background. This ensures that
absorption is the dominant process over scattering for the extinction of
light rays crossing the plumes (Beer–Lambert regime). The optical thickness
of the smokes was always smaller than 0.1 at our measurement wavelengths.
Exhaust plume NO2 SCD field
As explained in Sect. , the 2-D NO2 SCD field is
computed from at least two spectral images recorded at wavelengths showing a
significant difference of absorption cross section. To increase the
reliability of the measurements, four doublets of wavelengths were used:
λw1=441.8 and λs1=439.3; λw2=446.7 and
λs2=448.1; λw3=437.9 and
λs3=435.1;
λw4=465.8 and λs4=463.2. The automated acquisition
system was in charge of synchronizing the driving of the AOTF with the image
acquisition. A nominal acquisition sequence started by setting the
appropriate acoustic signal for the AOTF to filter at λw1, opening the CCD
shutter for 0.5 s, reading out the image and repeating these operations for the seven other wavelengths. After completion of the nominal sequence, a picture with
the AOTF turned off is taken and the nominal sequence is resumed. The dwell
time between the closing of the shutter and its reopening was 1.3 s,
yielding a total acquisition sequence duration of 13.1 s for the 8 spectral
images. In the plane of the stacks, the image footprint spans an area of
250×250 m2 with a 50 cm sampling.
The data analysis revealed that the images from the second and fourth doublets were
the less noisy because of a larger natural radiance and sensor sensitivity
compared to the wavelengths of doublets 1 and 3. Also, due to the plume
displacement over time (wind) and the presence of moving and changing
inhomogeneities across the plume (puffs, turbulent eddies), it was necessary
to perform time averaging (Sect. ). Indeed, the 1.3 s between two consecutive images is already a long time for features
moving at a typical 5 m s-1 speed (corresponding to 10 pixels per
second).
Figure shows the NO2 SCD field retrieved from
the averaging of images taken at λw2, λs2,
λw4 and λs4 (12 of each) using the method described in
Sect. . For each wavelength, the background signal
C0 was determined from image areas unaffected by the plume. The relative
error on C0 is about 0.5 % (estimated from the standard deviation
σC0 of the pixels sample yielding C0). Within this precision,
no variation of C0 across the FOV could be significantly detected. The
reason is the relatively small FOV of the camera (about 6∘) combined
with a high Sun at the time of the measurements (making the scene
illumination quite homogeneous). In Fig. , the
background grayscale image is the mean image at λw4, whereas the
pixels where the SCD is computed were selected based on the criterium
Cij<C0-2σC0. Investigating the random fluctuations observed in
various areas of the SCD field, one can estimate the detection limit to about
5×1016 molecules cm-2.
NO2 emission fluxes and synergies with SO2 cameras
The capability of resolving the NO2 SCD field with a high spatial and
temporal resolution provides new possibilities for the understanding of the
plume chemistry. Coal combustion processes yielding the formation of nitrogen
and sulfur species are well known , and several reactive
plume models can simulate the transport, formation and removal of these
species over different scales. These models are generally validated by
in situ air sampling at distances of several kilometers downwind (see for
instance ). Very few experiments attempted to
characterize the reactive content of the early plume, where the reactions are
still governed by the combustion products . In most cases,
a DOAS scanning system was used . The
same technique was also used for SO2, but to a lesser extent since
the introduction of filter-based SO2 cameras .
Recently, imaging Fourier transform spectroscopy (IFTS) demonstrated
capability for the measurement of a number of mid-infrared emitting species
such as CO2 and SO2 . However, NO, but not
NO2, can be retrieved with this technique.
An undisputed advantage of imaging systems with high temporal resolution is
their ability to track the displacement of remarkable features from one image
to another. We used the complete time series of spectral images (50 sequences
of 8 spectral images at a rate of 0.5 Hz) to determine the vertical speed of
the plume. This was done by tracking signal features created by local
increase or decrease of the NO2 concentration. On average, a vertical
speed of 4.8±0.5 m s-1 was observed. Furthermore, assuming a
Gaussian dispersion of the plume, one can infer a circular cross section from
the apparent width of the plume at each detector row (i.e., every 50 cm above
the stack outlet). As a result, a profile of emission flux (in g s-1)
can be drawn. Figure shows the NO2 emission flux as
a function of altitude up to a height above which the two plumes cannot be
discriminated anymore. The fluxes were calculated from the two SCD maps of
Fig. and both stacks. The increase is the result of
the conversion of NO into NO2 mainly by the reactions 2NO+O2→2NO2 and NO+HO2→NO2+OH
, even if these processes are balanced by the
photodissociation of NO2 as soon as it reaches open air under daylight (NO2+hν→NO+O). Qualitatively, these results
agree well with the increase reported by in a study of the
rate of increase of NO2 above power plant stacks. The analysis of
Fig. reveals that within the method approximations, the
NO2 concentration in the plume increases at a rate ranging from 0.75
to 1.25 g s-1 (9.8×1021–1.6×1022 molecules s-1) on average for the first 20 s.
NO2 flux computed through the plume horizontal cross section
as a function of altitude. Stacks height is 280 m. A symmetric Gaussian
dispersion is assumed up to the region of apparent intersection of the two
plumes.
The knowledge of the spatial distribution of NO2 can also prove
useful to correct measurements marked by interference from NO2. A
good example is with SO2 cameras where the SO2 SCD field is
retrieved by comparing the plume transmittance around 310 and 330 nm. In this
range, NO2 is also absorbing and its cross section roughly doubles
from 310 to 330 nm. Therefore, if both molecules are present in the plume,
the SO2 camera alone cannot distinguish their respective signatures.
So far, this interference has been overlooked in SO2 camera
validation exercises . In the case of the plumes
shown in Fig. for instance, a SO2 camera such
as the one used by would observe a
ΔτNO2=0.04 when the NO2 SCD reaches 3×1017 molecules cm-2. This variation of optical thickness corresponds
to a SO2 SCD of about 1.6×1017 molecules cm-2, which
is twice the detection limit reported in . Clearly, the
bias would increase with higher concentrations of NO2. Taking
advantage of the similar spatial resolution of both instruments, the
NO2 camera can provide a complete correction map for the SO2
data. On the temporal resolution side, however, the NO2 camera is, at
the moment, not capable of following the pace of SO2 cameras (1 Hz
typical), such that the correction maps would have to be applied to
temporally averaged SO2 data.
Conclusions
We have described a new passive atmospheric remote sensing instrument for the
measurement of NO2 SCDs above strong
sources. It is based on an AOTF which offers
a sufficient acceptance angle to be placed in an imaging system and the
necessary resolution for taking advantage of the fine structures of the
NO2 absorption cross section. The AOTF is electrically driven, such
that fast synchronized acquisitions of spectral images are possible.
The measurement principle is similar to the filter-based SO2 camera:
SCDs are retrieved from at least two spectral images taken at wavelengths
where absorption by the target molecule is significantly different.
Wavelengths are picked in the range 440–470 nm. Thanks to its higher spectral
resolution, the AOTF-based NO2 camera can perform its measurements
within a few nanometers. This makes the sensitivity to aerosols negligibly
small.
A mathematical framework for data processing has been developed, and the
different sources of error have been addressed. In applications focusing on
relatively high spatiotemporal resolution, the NO2 SCD detection
limit is about 5×1016 molecules cm-2. Different measurement
geometries offering longer integration times or more stable targets would
yield a lower limit.
The NO2 camera was successfully tested during the AROMAT-2 campaign
where measurements of NO2 SCD fields above the flue gas stacks of a
coal-fired power plant were performed with a temporal resolution of 3 min
and a spatial sampling of 50 cm (for a complete scene of 250×250 m2). Values up to 4×1017 molecules cm-2 were observed. The
quality of the data allowed us to clearly identify the conversion process from
NO to NO2 in the early plume, providing quantitative
information on the plume dynamic chemistry. In another example of
application, the measurements were used to show how the knowledge of the high-resolution NO2 field can help to correct SO2 camera data. If
overlooked, the interfering absorption of NO2 can yield a significant
bias in the retrieved SO2 SCDs. Other applications range from
emission monitoring to volcanic plume chemistry.
While the concept is mature, a number of improvement directions are still
being investigated. The most promising ones are the implementation of a
temperature feedback loop to reduce the uncertainty on the filtered
wavelength and the replacement of the CCD by a CMOS in order to reduce the
cooling needs and increase the temporal resolution of the measurements.